In [1]:
import numpy as np 
import pandas as pd 
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))
In [2]:
import plotly
import plotly.express as px
import plotly.graph_objects as go
import seaborn as sns
import matplotlib.pyplot as plt
from plotly import tools
from plotly.subplots import make_subplots
from plotly.offline import iplot, init_notebook_mode
init_notebook_mode()
In [3]:
df=pd.read_csv("Video_Games.csv")
In [4]:
df.head(7)
Out[4]:
Name Platform Year_of_Release Genre Publisher NA_Sales EU_Sales JP_Sales Other_Sales Global_Sales Critic_Score Critic_Count User_Score User_Count Developer Rating
0 Wii Sports Wii 2006.0 Sports Nintendo 41.36 28.96 3.77 8.45 82.53 76.0 51.0 8 322.0 Nintendo E
1 Super Mario Bros. NES 1985.0 Platform Nintendo 29.08 3.58 6.81 0.77 40.24 NaN NaN NaN NaN NaN NaN
2 Mario Kart Wii Wii 2008.0 Racing Nintendo 15.68 12.76 3.79 3.29 35.52 82.0 73.0 8.3 709.0 Nintendo E
3 Wii Sports Resort Wii 2009.0 Sports Nintendo 15.61 10.93 3.28 2.95 32.77 80.0 73.0 8 192.0 Nintendo E
4 Pokemon Red/Pokemon Blue GB 1996.0 Role-Playing Nintendo 11.27 8.89 10.22 1.00 31.37 NaN NaN NaN NaN NaN NaN
5 Tetris GB 1989.0 Puzzle Nintendo 23.20 2.26 4.22 0.58 30.26 NaN NaN NaN NaN NaN NaN
6 New Super Mario Bros. DS 2006.0 Platform Nintendo 11.28 9.14 6.50 2.88 29.80 89.0 65.0 8.5 431.0 Nintendo E
In [5]:
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 16719 entries, 0 to 16718
Data columns (total 16 columns):
 #   Column           Non-Null Count  Dtype  
---  ------           --------------  -----  
 0   Name             16717 non-null  object 
 1   Platform         16719 non-null  object 
 2   Year_of_Release  16450 non-null  float64
 3   Genre            16717 non-null  object 
 4   Publisher        16665 non-null  object 
 5   NA_Sales         16719 non-null  float64
 6   EU_Sales         16719 non-null  float64
 7   JP_Sales         16719 non-null  float64
 8   Other_Sales      16719 non-null  float64
 9   Global_Sales     16719 non-null  float64
 10  Critic_Score     8137 non-null   float64
 11  Critic_Count     8137 non-null   float64
 12  User_Score       10015 non-null  object 
 13  User_Count       7590 non-null   float64
 14  Developer        10096 non-null  object 
 15  Rating           9950 non-null   object 
dtypes: float64(9), object(7)
memory usage: 2.0+ MB
In [6]:
df.isna().sum()
Out[6]:
Name                  2
Platform              0
Year_of_Release     269
Genre                 2
Publisher            54
NA_Sales              0
EU_Sales              0
JP_Sales              0
Other_Sales           0
Global_Sales          0
Critic_Score       8582
Critic_Count       8582
User_Score         6704
User_Count         9129
Developer          6623
Rating             6769
dtype: int64
In [7]:
pd.unique(df['Platform'])
Out[7]:
array(['Wii', 'NES', 'GB', 'DS', 'X360', 'PS3', 'PS2', 'SNES', 'GBA',
       'PS4', '3DS', 'N64', 'PS', 'XB', 'PC', '2600', 'PSP', 'XOne',
       'WiiU', 'GC', 'GEN', 'DC', 'PSV', 'SAT', 'SCD', 'WS', 'NG', 'TG16',
       '3DO', 'GG', 'PCFX'], dtype=object)
In [8]:
code={'Wii':7,'GEN':4,'NES':3,'GB':6,'DS':7,'X360':7,'PS3':7,'PS2':6,'SNES':5,'GBA':6,'PS4':8,'3DS':8,'N64':5,'PS':5,'XB':6,'PC':8,'2600':2,'PSP':7,'XOne':8,'WiiU':8,'GC':6,'DC':6,'PSV':8,'SAT':5,'SCD':4,'WS':6,'NG':4,'TG16':4,'3DO':5,'GG':4,'PCFX':5}
df['Generation']=df['Platform'].map(code)
df
Out[8]:
Name Platform Year_of_Release Genre Publisher NA_Sales EU_Sales JP_Sales Other_Sales Global_Sales Critic_Score Critic_Count User_Score User_Count Developer Rating Generation
0 Wii Sports Wii 2006.0 Sports Nintendo 41.36 28.96 3.77 8.45 82.53 76.0 51.0 8 322.0 Nintendo E 7
1 Super Mario Bros. NES 1985.0 Platform Nintendo 29.08 3.58 6.81 0.77 40.24 NaN NaN NaN NaN NaN NaN 3
2 Mario Kart Wii Wii 2008.0 Racing Nintendo 15.68 12.76 3.79 3.29 35.52 82.0 73.0 8.3 709.0 Nintendo E 7
3 Wii Sports Resort Wii 2009.0 Sports Nintendo 15.61 10.93 3.28 2.95 32.77 80.0 73.0 8 192.0 Nintendo E 7
4 Pokemon Red/Pokemon Blue GB 1996.0 Role-Playing Nintendo 11.27 8.89 10.22 1.00 31.37 NaN NaN NaN NaN NaN NaN 6
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
16714 Samurai Warriors: Sanada Maru PS3 2016.0 Action Tecmo Koei 0.00 0.00 0.01 0.00 0.01 NaN NaN NaN NaN NaN NaN 7
16715 LMA Manager 2007 X360 2006.0 Sports Codemasters 0.00 0.01 0.00 0.00 0.01 NaN NaN NaN NaN NaN NaN 7
16716 Haitaka no Psychedelica PSV 2016.0 Adventure Idea Factory 0.00 0.00 0.01 0.00 0.01 NaN NaN NaN NaN NaN NaN 8
16717 Spirits & Spells GBA 2003.0 Platform Wanadoo 0.01 0.00 0.00 0.00 0.01 NaN NaN NaN NaN NaN NaN 6
16718 Winning Post 8 2016 PSV 2016.0 Simulation Tecmo Koei 0.00 0.00 0.01 0.00 0.01 NaN NaN NaN NaN NaN NaN 8

16719 rows × 17 columns

In [9]:
fig = px.sunburst(df, path=['Generation', 'Platform'], values='Global_Sales',
                  color='Generation', title = 'Global Sales By Console generations')
fig.show()
In [10]:
fig = px.histogram(df, x="Platform", title = 'Number of Games produced By each Platform')
fig.show()
In [11]:
fig = px.histogram(df, x="Genre", color = 'Genre', title = 'Total number of Games in each Genre')
fig.show()
In [12]:
fig = px.scatter(df, x="Critic_Score", y="Global_Sales", color="Genre", hover_name = 'Name', title = 'Global Sales vs critic score')
fig.show()
In [13]:
fig = px.scatter(df, x="Critic_Score", y="NA_Sales", color="Genre", hover_name = 'Name', title = 'North American Sales vs critic score')
fig.show()
In [14]:
fig = px.scatter(df, x="Critic_Score", y="EU_Sales", color="Genre", hover_name = 'Name', title = 'European Sales vs critic score')
fig.show()
In [15]:
fig = px.scatter(df, x="Critic_Score", y="JP_Sales", color="Genre", hover_name = 'Name', title = 'Japanese Sales vs critic score')
fig.show()
In [16]:
fig = px.scatter(df, x="Critic_Score", y="Other_Sales", color="Genre", hover_name = 'Name', title = 'Other Sales vs critic score')
fig.show()
In [17]:
fig = px.scatter(df, x="Year_of_Release", y="Global_Sales", color="Genre", hover_name = 'Name', title = 'Global sales Genre wise from 1980 to 2020')
fig.show()
In [18]:
fig = px.pie(df, values='Global_Sales', names='Generation', title='Global sales shares by each Generation')
fig.show()
In [19]:
plt.figure(figsize=(12,10))
sns.heatmap(df.corr(), annot = True, fmt= '.2f') 
plt.show()
In [20]:
fig = px.scatter(df, x="Rating", y="Global_Sales", color="Rating", hover_name = 'Name', title = 'sales of games with a certain rating')
fig.show()
In [ ]:
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